At Giant Oak, we seek to make the world a better place by developing analytics for risk mitigation in large data environments. We take an interdisciplinary view of the social sciences, using theories and methodologies from economics, computer science, and other fields to develop innovative tools and create knowledge at the frontiers of current understanding. We specialize in going beyond mainstream approaches, creating a front line of custom solutions for unique data challenges. We work with our clients to create products which analyze large datasets in order to advance security goals on issues such as financial threats, insurgency, crime and terrorism.


Giant Oak has openings for social science explorers with a passion for national security challenges that range from financial fraud to human trafficking. Candidates must have a demonstrated proficiency in quantitative social science methods and a PhD in economics or a related discipline. Candidates should have the ability to think creatively, build models, and work with a variety of data in one or more open-source programming languages. Ideal candidates will be comfortable working with “big data” and have experience with large-scale data manipulation, analytic tools, and data visualizations. Other desired qualifications include experience with machine learning techniques, social network or graph theoretic analysis, distributed and/or graph databases (Cassandra, HBase, Titan, etc.), natural language processing (NLP), information extraction (IE), ETL, and cloud computing.


- Ph.D. in a relevant social science discipline or a quantitative discipline (e.g., Computer Science, Electrical Engineering, Applied Mathematics, or Statistics) with applications to social systems.
- Experience in a data-intensive analytical environment.
- Fluent in one or more programming languages (e.g., Python, Java, C++).
- Applicants must be eligible for a U.S. Security Clearance. 


Application Requirements

  • Cover Letter
  • CV
  • Job Market Paper
  • Letters of Reference
  • Code Sample or GitHub Link



Apply here.